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We continued the trip, going to Brazil to learn Portuguese, China to learn Mandarin, and South Korea to learn Korean. Asia proved a far harder task than Spain or Brazil. In our preparation, we had assumed those languages would be only a little more difficult than the European ones, although it turned out that they were much harder. As a result, our no-English rule was starting to crack, although we still applied it as much as we could. Even if our Mandarin and Korean didn’t reach the same level of ability after a short stay, it was still enough to make friends, travel, and converse with people on a variety of topics. At the end of our year, we could confidently say we spoke four new languages.
Having seen the same approach work for academic computer science and language-learning adventures, I was slowly becoming convinced that it could be applied to much more. I had enjoyed drawing as a kid, but like most people’s attempts, any faces I drew looked awkward and artificial. I had always admired people who could quickly sketch a likeness, whether it be street-side caricaturists to professional portrait painters. I wondered if the same approach to learning MIT classes and languages could also apply to art.
I decided to spend a month improving my ability to draw faces. My main difficulty, I realized, was in placing the facial features properly. A common mistake when drawing faces, for instance, is putting the eyes too far up the head. Most people think they sit in the top two-thirds of the head. In truth, they’re more typically halfway between the top of the head and the chin. To overcome these and other biases, I did sketches based on pictures. Then I would take a photo of the sketch with my phone and overlay the original image on top of my drawing. Making the photo semitransparent allowed me to see immediately whether the head was too narrow or wide, the lips too low or too high or whether I had put the eyes in the right spot. I did this hundreds of times, employing the same rapid feedback strategies that had served me well with MIT classes. Applying this and other strategies, I was able to get a lot better at drawing portraits in a short period of time (see below).
courtesy of the author
UNCOVERING THE ULTRALEARNERS
On the surface, projects such as Benny Lewis’s linguistic adventures, Roger Craig’s trivia mastery, and Eric Barone’s game development odyssey are quite different. However, they represent instances of a more general phenomenon I call ultralearning.* As I dug deeper, I found more stories. Although they differed in the specifics of what had been learned and why, they shared a common thread of pursuing extreme, self-directed learning projects and employed similar tactics to complete them successfully.
Steve Pavlina is an ultralearner. By optimizing his university schedule, he took a triple course load and completed a computer science degree in three semesters. Pavlina’s challenge long predated my own experiment with MIT courses and was one of the first inspirations that showed me compressing learning time might be possible. Done without the benefit of free online classes, however, Pavlina attended California State University, Northridge, and graduated with actual degrees in computer science and mathematics.
Diana Jaunzeikare embarked on an ultralearning project to replicate a PhD in computational linguistics. Benchmarking Carnegie Mellon University’s doctoral program, she wanted to not only take classes but also conduct original research. Her project had started because going back to academia to get a real doctorate would have meant leaving the job she loved at Google. Like many other ultralearners before her, Jaunzeikare’s project was an attempt to fill a gap in education when formal alternatives didn’t fit with her lifestyle.
Facilitated by online communities, many ultralearners operate anonymously, their efforts observable only by unverifiable forum postings. One such poster at Chinese-forums.com, who goes only by the username Tamu, extensively documented his process of studying Chinese from scratch. Devoting “70–80+ hours each week” over four months, he challenged himself to pass the HSK 5, China’s second highest Mandarin proficiency exam.
Other ultralearners shed the conventional structures of exams and degrees altogether. Trent Fowler, starting in early 2016, embarked on a yearlong effort to become proficient in engineering and mathematics. He titled it the STEMpunk Project, a play on the STEM fields of science, technology, engineering, and mathematics he wanted to cover and the retrofuturistic steampunk aesthetic. Fowler split his project into modules. Each module covered a particular topic, including computation, robotics, artificial intelligence, and engineering, but was driven by hands-on projects instead of copying formal classes.
Every ultralearner I encountered was unique. Some, like Tamu, preferred punishing, full-time schedules to meet harsh, self-imposed deadlines. Others, like Jaunzeikare, managed their projects on the side while maintaining full-time jobs and work obligations. Some aimed at the recognizable benchmarks of standardized exams, formal curricula, and winning competitions. Others designed projects that defied comparison. Some specialized, focusing exclusively on languages or programming. Others desired to be true polymaths, picking up a highly varied set of skills.
Despite their idiosyncrasies, the ultralearners had a lot of shared traits. They usually worked alone, often toiling for months and years without much more than a blog entry to announce their efforts. Their interests tended toward obsession. They were aggressive about optimizing their strategies, fiercely debating the merits of esoteric concepts such as interleaving practice, leech thresholds, or keyword mnemonics. Above all, they cared about learning. Their motivation to learn pushed them to tackle intense projects, even if it often came at the sacrifice of credentials or conformity.
The ultralearners I met were often unaware of one another. In writing this book, I wanted to bring together the common principles I observed in their unique projects and in my own. I wanted to strip away all the superficial differences and strange idiosyncrasies and see what learning advice remains. I also wanted to generalize from their extreme examples something an ordinary student or professional can find useful. Even if you’re not ready to tackle something as extreme as the projects I’ve described, there are still places where you can adjust your approach based on the experience of ultralearners and backed by the research from cognitive science.
Although the ultralearners are an extreme group of people, this approach to things holds potential for normal professionals and students. What if you could create a project to quickly learn the skills to transition to a new role, project, or even profession? What if you could master an important skill for your work, as Eric Barone did? What if you could be knowledgeable about a wide variety of topics, like Roger Craig? What if you could learn a new language, simulate a university degree program, or become good at something that seems impossible to you right now?
Ultralearning isn’t easy. It’s hard and frustrating and requires stretching outside the limits of where you feel comfortable. However, the things you can accomplish make it worth the effort. Let’s spend a moment trying to see what exactly ultralearning is and how it differs from the most common approaches to learning and education. Then we can examine what the principles are that underlie all learning, to see how ultralearners exploit them to learn faster.
What exactly is ultralearning? While my introduction to the eclectic group of intense autodidacts started with seeing examples of unusual learning feats, СКАЧАТЬ